U.S. patent application number 16/102233 was filed with the patent office on 2018-12-06 for system and method for dynamically controlling sample rates and data flow in a networked measurement system by dynamic determination of statistical significance.
This patent application is currently assigned to Oracle America, Inc.. The applicant listed for this patent is Oracle America, Inc.. Invention is credited to Suryansh Agarwal, Daniel E. Fichter, Nikki K. Gomez, Jonah Goodhart, Aniq Rahman, Michael Garrett Seiler, Christopher R. Tsoufakis.
Application Number | 20180351834 16/102233 |
Document ID | / |
Family ID | 55410561 |
Filed Date | 2018-12-06 |
United States Patent
Application |
20180351834 |
Kind Code |
A1 |
Fichter; Daniel E. ; et
al. |
December 6, 2018 |
SYSTEM AND METHOD FOR DYNAMICALLY CONTROLLING SAMPLE RATES AND DATA
FLOW IN A NETWORKED MEASUREMENT SYSTEM BY DYNAMIC DETERMINATION OF
STATISTICAL SIGNIFICANCE
Abstract
A system and methods for dynamically controlling sample rates
and data flow in a distributed networked environment by dynamic
determination of statistical significance or characteristics for an
unlimited number of data collection scripts concurrently executed
on concurrently rendering web pages operating an unlimited number
of advertisements. Consumer and media behaviors are sampled on all
the different components of the distributed environment to gather
information, which is transmitted to a downstream statistical
analytics system. The system and methods are configured to balance
the communication data flow and load among servers and browsers in
this distributed networked environment that are engaged in viewing
of online content including online content with one or more
advertisements.
Inventors: |
Fichter; Daniel E.; (New
York, NY) ; Tsoufakis; Christopher R.; (Salt Lake
City, UT) ; Gomez; Nikki K.; (Brooklyn, NY) ;
Rahman; Aniq; (Redwood Shores, CA) ; Goodhart;
Jonah; (New York, NY) ; Seiler; Michael Garrett;
(Scarsdale, NY) ; Agarwal; Suryansh; (West Orange,
NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Oracle America, Inc. |
Redwood Shores |
CA |
US |
|
|
Assignee: |
Oracle America, Inc.
Redwood Shores
CA
|
Family ID: |
55410561 |
Appl. No.: |
16/102233 |
Filed: |
August 13, 2018 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15483821 |
Apr 10, 2017 |
10075350 |
|
|
16102233 |
|
|
|
|
15063199 |
Mar 7, 2016 |
9621472 |
|
|
15483821 |
|
|
|
|
14205115 |
Mar 11, 2014 |
9282048 |
|
|
15063199 |
|
|
|
|
61785930 |
Mar 14, 2013 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 47/25 20130101;
H04L 43/04 20130101; H04L 43/02 20130101 |
International
Class: |
H04L 12/26 20060101
H04L012/26; H04L 12/825 20130101 H04L012/825 |
Claims
1. A computer-implemented method for dynamically adjusting sample
rates corresponding to webpage content, the method comprising:
designating a target sample rate for gathering sample data
corresponding to presentations of and interactions with online
content on a webpage, wherein the online content includes at least
one of text, picture and video; facilitating execution of a script
that is configured to: randomly select a subset of a plurality of
content objects associated with a web site, wherein the script
identifies a size of the subset based on the target sample rate;
and collect, for each content object in the subset, data that
characterizes a presentation of or interaction associated with the
content object; generating a statistical-accuracy metric based on
the data collected for the subset; determining, based on the
statistical-accuracy metric, that additional data is to be
collected using the script; adjusting the target sample rate to a
higher target sample rate; and facilitating subsequent execution of
the script that uses the higher target sample rate to identify
another size of a subsequent subset to be randomly selected for
which other data is to be collected.
2. The method of claim 1, wherein facilitating execution of the
script includes transmitting the script and the target sample rate
to a user device, and wherein facilitating subsequent execution of
the script that uses the higher target sample rate includes
transmitting the higher target sample rate to the user device.
3. The method of claim 1, wherein facilitating execution of the
script includes transmitting the script and the target sample rate
to a set of user devices, wherein facilitating subsequent execution
of the script that uses the higher target sample rate includes
transmitting the higher target sample rate to at least some of the
set of user devices, and wherein the method further includes:
receiving, from each user device of the set of user devices, the
data collected at the user device, wherein the data received from a
first user device of the set of user devices corresponds to a first
subset of the plurality of content objects that is different than a
second subset of the plurality of content objects to which the data
received from a second user device of the set of user devices.
4. The method of claim 1, wherein facilitating execution of the
script includes executing the script, and wherein facilitating
subsequent execution of the script that uses the higher target
sample rate includes executing the script using the higher target
sample rate.
5. The method of claim 1, wherein the determination that additional
data is to be collected using the script is made at a user device
via execution of the script.
6. The method of claim 1, wherein execution of the script includes
execution of the script within a browser or application on a user
device.
7. The method of claim 1, wherein the collected data includes an
in-view time or in-view rate.
8. The method of claim 1, wherein generating the
statistical-accuracy metric includes: identifying, for each content
object of the plurality of content objects, identifying a number of
samples corresponding to data having been collected for the content
object; and generating a distribution of the numbers of samples
identified for the plurality of content objects, wherein the
statistical-accuracy metric based on the distribution and a target
distribution.
9. A computer-program product tangibly embodied in a non-transitory
machine-readable storage medium, including instructions configured
to cause one or more data processors to perform actions including:
designating a target sample rate for gathering sample data
corresponding to presentations of and interactions with online
content on a webpage, wherein the online content includes at least
one of text, picture and video; facilitating execution of a script
that is configured to: randomly select a subset of a plurality of
content objects associated with a web site, wherein the script
identifies a size of the subset based on the target sample rate;
and collect, for each content object in the subset, data that
characterizes a presentation of or interaction associated with the
content object; generating a statistical-accuracy metric based on
the data collected for the subset; determining, based on the
statistical-accuracy metric, that additional data is to be
collected using the script; adjusting the target sample rate to a
higher target sample rate; and facilitating subsequent execution of
the script that uses the higher target sample rate to identify
another size of a subsequent subset to be randomly selected for
which other data is to be collected.
10. The computer-program product of claim 9, wherein facilitating
execution of the script includes transmitting the script and the
target sample rate to a user device, and wherein facilitating
subsequent execution of the script that uses the higher target
sample rate includes transmitting the higher target sample rate to
the user device.
11. The computer-program product of claim 9, wherein facilitating
execution of the script includes transmitting the script and the
target sample rate to a set of user devices, wherein facilitating
subsequent execution of the script that uses the higher target
sample rate includes transmitting the higher target sample rate to
at least some of the set of user devices, and wherein the actions
further include: receiving, from each user device of the set of
user devices, the data collected at the user device, wherein the
data received from a first user device of the set of user devices
corresponds to a first subset of the plurality of content objects
that is different than a second subset of the plurality of content
objects to which the data received from a second user device of the
set of user devices.
12. The computer-program product of claim 9, wherein facilitating
execution of the script includes executing the script, and wherein
facilitating subsequent execution of the script that uses the
higher target sample rate includes executing the script using the
higher target sample rate.
13. The computer-program product of claim 9, wherein the
determination that additional data is to be collected using the
script is made at a user device via execution of the script.
14. The computer-program product of claim 9, wherein execution of
the script includes execution of the script within a browser or
application on a user device.
15. The computer-program product of claim 9, wherein the collected
data includes an in-view time or in-view rate.
16. The computer-program product of claim 9, wherein generating the
statistical-accuracy metric includes: identifying, for each content
object of the plurality of content objects, identifying a number of
samples corresponding to data having been collected for the content
object; and generating a distribution of the numbers of samples
identified for the plurality of content objects, wherein the
statistical-accuracy metric based on the distribution and a target
distribution.
17. One or more processors in one or more servers coupled to a
network and a plurality of user devices adapted to access a web
page for display on a website hosted on a particular server; memory
storing instructions that cause the one or more processors to
performing a set of actions including: designating a target sample
rate for gathering sample data corresponding to presentations of
and interactions with online content on a webpage, wherein the
online content includes at least one of text, picture and video;
facilitating execution of a script that is configured to: randomly
select a subset of a plurality of content objects associated with a
web site, wherein the script identifies a size of the subset based
on the target sample rate; and collect, for each content object in
the subset, data that characterizes a presentation of or
interaction associated with the content object; generating a
statistical-accuracy metric based on the data collected for the
subset; determining, based on the statistical-accuracy metric, that
additional data is to be collected using the script; adjusting the
target sample rate to a higher target sample rate; and facilitating
subsequent execution of the script that uses the higher target
sample rate to identify another size of a subsequent subset to be
randomly selected for which other data is to be collected.
18. The system of claim 17, wherein facilitating execution of the
script includes transmitting the script and the target sample rate
to a user device, and wherein facilitating subsequent execution of
the script that uses the higher target sample rate includes
transmitting the higher target sample rate to the user device.
19. The system of claim 17, wherein facilitating execution of the
script includes transmitting the script and the target sample rate
to a set of user devices, wherein facilitating subsequent execution
of the script that uses the higher target sample rate includes
transmitting the higher target sample rate to at least some of the
set of user devices, and wherein the actions further include:
receiving, from each user device of the set of user devices, the
data collected at the user device, wherein the data received from a
first user device of the set of user devices corresponds to a first
subset of the plurality of content objects that is different than a
second subset of the plurality of content objects to which the data
received from a second user device of the set of user devices.
20. The system of claim 17, wherein facilitating execution of the
script includes executing the script, and wherein facilitating
subsequent execution of the script that uses the higher target
sample rate includes executing the script using the higher target
sample rate.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/483,821, filed on Apr. 10, 2017, which is a
continuation of U.S. patent application Ser. No. 15/063,199, filed
Mar. 7, 2016, which is a continuation of U.S. patent application
Ser. No. 14/205,115, filed on Mar. 11, 2014, which claims the
benefit of and priority to U.S. Provisional Application No.
61/785,930, filed on Mar. 14, 2013. Each of these applications is
hereby incorporated by reference in its entirety for all
purposes.
BACKGROUND OF THE INVENTION
1. Field of the Invention
[0002] The present invention relates to determining advertising
("ad") and content visibility and other indications of attention to
or engagement with advertising or content both within servers, and
on network connections. In particular, the present invention
relates to computer systems and methods for measuring user behavior
on web-connected devices and dynamically controlling sample rates
and data flow in a networked measurement system by dynamic
determination of statistical significance or statistical
characteristics (e.g. threshold). Consumer and media behaviors are
sampled to gather information, which is transmitted to a downstream
analytics system.
2. Description of the Related Art
[0003] The Internet and other types of on-line communication have
become increasingly popular to the point where they now compete
with traditional media such as print media and broadcast media for
the attention of users. Due to the extra large amount of web pages
available for users to view worldwide, online content creation and
publication have become a huge business.
[0004] Yet, the data flows created by millions of browsers and
display advertisements being sampled simultaneously are
significant, costly, and push the capacity of current technology to
its limits.
[0005] It is therefore advantageous to have a way to reduce or
regulate data flows in parts of the systems of the current
technology. Although on the surface, one solution may be to reduce
the sample rates for samples of user and media behaviors to reduce
the amounts of data flow to the analytic servers, and within the
analytic servers. However, reducing sample rates also results in
statistical inaccuracy, thereby compromising the overall integrity
of the systems and methods involved with the data flow.
[0006] It would therefore be advantageous to limit aggregate data
flows from distributed browsers and within servers by limiting
sample rates in a way that maintains sufficient statistical
significance, thereby not impacting the integrity of the systems
and methods. Yet, this goal has been difficult to accomplish
because raw data is gathered from thousands, if not millions of
different locations concurrently. Yet the results of testing for
significance are normally based on the aggregate number of samples
for a particular element that is to be sampled.
SUMMARY OF THE INVENTION
[0007] In one innovative aspect, the present invention provides a
system and methods for measuring user behavior and engagement with
online content including online content with advertising on
web-connected devices and dynamically controlling sample rates and
data flow in a networked measurement system by dynamic
determination of statistical significance or characteristics (e.g.,
a threshold). In some implementations, a threshold is determined by
a statistical fit to an ideal distribution. In some
implementations, a threshold may be determined based on assessing a
required capacity of downstream computers or servers. In some
instances, a threshold may be determined by using both criteria in
combination. Consumer (user or client) and media behaviors are
sampled at varying rates to gather information, which is
transmitted to a downstream analytics system for determining the
sample rates needed to attain statistical accuracy for an unlimited
number of scripts concurrently executed on hundreds of millions of
concurrently rendering web pages operating an unlimited number of
advertisements. The system and methods advantageously create a way
to balance the communication flow and load among servers and
browsers engaged in advertisement viewing, to a desired aggregate
level, by controlling the sample rates for samples being created on
all operating browsers, and the sampling of data that has already
been-collected or gathered within the user-behavior analytic server
itself.
[0008] In another innovative aspect, the system and methods of the
present invention sample data in real time by a script that is
operated on each browser or application. In one implementation, the
system and methods include a decision capability within the script
to allow it to operate autonomously and refrain from sending
sampling data to the downstream analytic servers (e.g., statistical
analytics or user-behavior measurement servers) if instructed by
another computer. It should be recognized that in any situation
where an instance of the script running on one web page does not
transmit data pertaining to a sample, it is in effect lowering the
sample rate. However, because no given instance of a single script
operating on one device has the ability to view all instances of
all elements being sampled (for example, advertisements,
publishers, users, etc.), an instance on each device normally must
interrogate an analytic server to determine if it is required to
send data, and if so, identify the data that needs to be sent. It
should be recognized that these tasks maintain statistical
significance for the element being sampled. In accordance with the
present invention, the script transmits only a named subset of the
data it would normally produce in a sample. This unique capability
is significant and advantageous because it configures varying
control over data flow, and it configures the system and methods to
record data about situations relative to which no sample was
obtained.
[0009] In accordance with yet another innovative aspect, a browser
running a script may make an independent determination not to send
data to a statistical or user-behavior analytics server, without
receiving information from it. In such instances, the script may
have viewed a particular advertisement enough times in a given
instance of its execution to be reasonably certain that additional
samples will not impact statistical significance.
[0010] In accordance with still another aspect of the present
invention, the script is configured in advance with the sample rate
by a preset (hard-coded) instruction in the script. Alternatively,
the script may be configured to be informed of it. In such
implementations, the script is not required to communicate with the
user-behavior analytics server about its sampling behavior.
[0011] In all implementations in accordance with the present
invention, no matter how the script receives or is apprised of the
desired sample rate, the script randomizes among relevant objects
to sample (for example, advertisements) to accomplish the desired
sample rate.
[0012] Yet another aspect of the present invention is a capability
of the analytics server (e.g., statistical or user-behavior
measurement system) to determine whether to adjust or lower sample
rates based on inbound communication that is received, data
processing, or evaluating other capacity constraints that may
relate to the aggregate flow of data from various scripts operating
on browsers, to the analytic servers (e.g., statistical analytics
or user-behavior measurement servers).
[0013] In some implementations of the present invention, the script
behavior is configured or controlled, by programming, to determine
the information at every invocation, to be sent to a computing
device for analysis (e.g., statistical analytics or user-behavior
measurement servers). In a similar vein, the computing device is
also configured or controlled, by programming, to determine the
data to be sent for further analysis, downstream in the networked
environment. The mechanisms of the present invention inform
processes of the optimal sample rate that balance processing and
network costs with statistical significance. In some
implementations, samples are weighted in downstream processing, by
applying the multiplicative inverse of the net sample rate, taking
into account the sampling occurring on either or both of the
statistical analytics server and the browsers.
[0014] The system and methods of the present invention may be
implemented on one or more computer program products and may
provide a user interface for display to a user, wherein the user
interface enables users to use tools to view advertising and
otherwise provide data that may be used for sampling.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The present invention is illustrated by way of example, and
not by way of limitation in the figures of the accompanying
drawings in which like reference numerals are used to refer to
similar elements.
[0016] FIG. 1 is a block diagram illustrating an embodiment of a
system for dynamically controlling sample rates and data flow in a
networked measurement system by dynamic determination of
statistical significance in accordance with the present
invention.
[0017] FIG. 2A is a block diagram illustrating various hardware
components of an example user-behavior analytics server in
accordance with the present invention.
[0018] FIG. 2B is a block diagram illustrating various software
components of the example user-behavior analytics server in
accordance with the present invention.
[0019] FIG. 3A is a block diagram illustrating various
hardware/software components of an example client device.
[0020] FIG. 3B is a block diagram illustrating an example data
collection script.
[0021] FIG. 4 is a flowchart of an example general method for
dynamically controlling sample rates and data flow in a networked
measurement system by dynamic determination of statistical
significance in accordance with the present invention.
[0022] FIGS. 5A and 5B together illustrate a flowchart of an
example method illustrating the sampling process as performed from
the perspective of the data collection script.
[0023] FIG. 6 is a flowchart illustrating an example method of the
sampling process from a perspective of the analytic server
performed in accordance with the present invention.
[0024] FIG. 7 is a block diagram illustrating one embodiment of
data storage in accordance with the present invention.
DETAILED DESCRIPTION OF EMBODIMENTS
[0025] FIG. 1 illustrates a block diagram of one embodiment of the
system 100 for dynamically controlling sample rates and data flow
in a networked measurement system by dynamic determination of
statistical significance or characteristics (e.g., a threshold).
Consumer and media behaviors are sampled to gather information,
which is transmitted to a downstream analytics system of the
present invention. In some implementations, a threshold may be
determined by a statistical fit to an ideal distribution. In some
implementations, a threshold may be determined based on assessing
required or possible capacity levels determined for downstream
computers or servers. In some instances, a threshold may be
determined by using both criteria in combination.
[0026] The system 100 dynamically controls sample rates and data
flow of online content including online content with one or more
advertisements. The system and methods of the present invention
described here either utilize or are operated on one or more
computing systems (with one or more computers, processors, and data
storage devices) that are configured to communicate in a
distributed environment. For many examples described in the
specification below, online content can be any text, picture or
video created and/or published by publishers on web pages which are
accessible to users. Furthermore, for many examples in the
specification below, an online advertisement ("ad") is any text,
picture or video whose purpose is advertising communication
including any flash asset, any image of Internet Advertising Board
(IAB) or industry standard width and height that is clickable
including any recursion into iframes from the original page.
[0027] The illustrated system 100 includes a script server 101, a
content server 102, an ad server 111, a statistical analytics
server 104, a data collection server 116, and one or more client
devices 107a-107n that are accessed by users indicated by reference
numerals 114a-114n. In the illustrated embodiment, these entities
are communicatively coupled via a network 103. Although only three
client devices 107a-n are illustrated, it should be recognized that
any number of client devices 107n are available to any number of
users 114n. Furthermore, while only one network 103 is coupled to
the script server 101, the content server 102, the statistical
analytics server 104, the ad server 111, the data collection server
111, and the one or more client devices 107a-107n, in practice any
number of networks 103 can be connected to these entities. In one
embodiment, the script server 101, the training rules server 106,
the analytics server 104, the content server 102 and the ad server
111 are hardware servers including a processor, memory, and network
communication capabilities.
[0028] The system 100 advantageously samples data in real time by a
data collection script 110 that runs on each web browser 108 or
application on any client or user's device 107a, 107b, or 107n.
This data collection script 110 may be installed on each browser or
application and is configured with a capability to stop or not send
data to any downstream analytic servers (e.g., the statistical
analytics server 104) upon receiving instructions from another
computer. In any situation where an instance of the data collection
script 110 running on one web page 108 does not transmit data
pertaining to a sample, it in effect lowers the sample rate. No
given instance of the data collection script 110 has the ability to
view all instances of every single element being sampled (for
example, advertisements, publishers, users etc.), hence any
instance in the data collection script 110 must interrogate the
statistical analytic server 104 to determine if it is required to
send data, and identify the data that must be sent, in order to
maintain statistical significance for that element. In addition,
the data collection script 110 can transmit only a subset of the
data as a sample that is identified for it. With this capability,
the system of the present invention exercises varying control over
data flow and records data on situations in which no samples are
taken.
[0029] A web browser 108 running a data collection script 110 is
configured to make an independent decision to not send data in
instances where it does not receive information from the
statistical analytics server 104. For such instances, the data
collection script 110 must have seen a particular advertisement (or
other online content) displayed enough times in a given instance of
its execution to be reasonably certain that obtaining additional
samples will not impact statistical significance. In other
instances, the data collection script 110 may be configured with a
preset (hard-coded) instruction in the data collection script 110,
which informs on the sample rate. In this particular
implementation, the data collection script does not need to
communicate with the statistical analytics server 104 regarding its
sampling behavior. In all the implementations, regardless of how
the data collection script 110 is configured to receive the desired
sample rate, the data collection script 110 is configured to
randomize among relevant objects to sample (normally
advertisements) such that the desired sample rate is
accomplished.
[0030] In operation, the data collection script 110 configured to
run on a web page 109, rendering in the browser 108 on a client
device 107 a-n, is loaded from the script server 101, and begins to
execute. The data collection script 110 once loaded, first finds
objects of interest on the web page 109. These objects of interest
could be advertisements or other content elements. Derived
characteristics of the content elements, for example, "in-view
time," "in-view rate," etc., are taken as numerical samples. It
should be recognized that any one of several metrics may be sampled
and those that are referred to here are by example.
[0031] The data collection script 110 is configured to determine if
sampling is required, by attempting to access a document provided
by the content server 102 connected to the network 103 and
containing instructions and recommended sample rates. In the event
no document is returned, the data collection script 110 uses a
"default" sample rate, which is embedded into the data collection
script programming itself. If the "default" sample rate is
determined to be 100%, then no sampling may be required for the
time being. In the event sampling is required, the data collection
script 110 is configured to randomly select objects to measure from
the objects of interest discovered at a rate corresponding to the
sample rate. For example, if the sample rate is determined to be
10%, then 1/10.sup.th of the available objects are sampled. The
objects to be sampled are selected by a random number.
[0032] After the data collection script 110 writes data to the
statistical analytics server 104, it may decide to further sample
data, as it is aware of its own capacity.
[0033] In some implementations, the statistical analytics server
104 is configured to determine whether it needs to lower sample
rates based on inbound communication, data processing, or other
capacity constraints related to the aggregate flow of data from
data collection scripts 110 (on the various client or user devices
107a-17n) running on web browsers 108 to the statistical analytic
servers 104.
[0034] The network 103 is a conventional type, wired or wireless,
and may have any number of configurations such as a star
configuration, token ring configuration or other configurations.
Furthermore, the network 103 may comprise a local area network
(LAN), a wide area network (WAN) (e.g., the Internet), and/or any
other interconnected data path across which multiple devices may
communicate. In yet another embodiment, the network 103 may be a
peer-to-peer network. The network 103 may also be coupled to or
includes portions of a telecommunications network for sending data
in a variety of different communication protocols. In yet another
embodiment, the network 103 includes Bluetooth communication
networks or a cellular communications network for sending and
receiving data such as via short messaging service (SMS),
multimedia messaging service (MMS), hypertext transfer protocol
(HTTP), direct data connection, WAP, email, etc.
[0035] The client device 107a is representative of client devices
107a-107n and is a conventional type of computing device, for
example, a personal computer, a hardware server, a laptop computer,
a tablet computer, or smart phone. The client devices 107a-107n,
are coupled to the network 103 by signal lines 116a, 116b-116n,
respectively. In one embodiment, the client device 107 is coupled
to receive (e.g., download or otherwise view) content with online
advertisements from the ad server 111 and other content from
publishing sites or third party servers (not shown) but coupled in
the illustrated distributed environment. The client device 107
includes the web browser 108 for presenting web pages 109 including
online content and advertisements to the user or client 114a, 114b,
through 114n for viewing on their respective client devices
107a-107n. The web browser 108 on each of the client or user device
107a-107n presents advertisements and other online content, and
receives input from the user or client 114a-114n as represented by
signal lines 112a-112n. The signal lines 112a-112n represent
interactions of the users, 114a-114n, with their respective devices
107a-17n (e.g., viewing or manipulating tools to receive or control
viewing of the online content). The web browser 108 and the data
collection script 110 are operable on the client devices 107a
through 17n.
[0036] In one embodiment, the data collection script 110 may be
embedded on the web browser 108 by the script server 101. In
another embodiment, the data collection script 110 may be placed on
the web browser 108 by the ad server 111. In yet another
embodiment, the data collection script 110 may be embedded on the
web browser 108 by the content server 102.
[0037] The script server 101 is a computer program running on a
hardware system for providing one or more data collection scripts
110 (configured to determine or measure user behavior of visibility
of online advertisement content) to web pages 109. For example, the
script server 101 may be a web server that creates and provides
data collection scripts for publishers to place the scripts on web
browsers 108. In one embodiment, the script server 101 may provide
the data collection script 110 to a publisher that places the data
collection script 110 on a web browser 108 that provides a web page
containing content including advertisements for viewing by users or
clients 114a-114n. In another embodiment, the ad server 111 is used
to place the data collection script 110 on the web browser 108. In
yet another embodiment, the content server 102 is used to place the
data collection script 110 on the web browser 108. The script
server 101 is coupled to the network 103, by signal line 120, for
providing data collection scripts 110 to be placed on the web
browsers 108.
[0038] The statistical analytics server 104 is a computer program
running on a hardware system for dynamically controlling sample
rates and data flow in the networked system by dynamic
determination of statistical significance or characteristics.
Consumer and media behaviors are sampled to gather information,
which is transmitted to the statistical analytics server 104. For
example, the statistical analytics server 104 may be a web server
that receives samples of data obtained by the data collection
script 110 operating on the client devices 107a-107n. The
statistical analytics server 104 is coupled to the network 103, by
signal line 122, for communication with the other components of the
system 100.
[0039] The ad server 111 is a computer program running on a
hardware system for placing advertisements on websites and/or
placing the data collection script 110 on web pages 109. For
example, the ad server 111 may be a web server that receives
advertisements from the ad preparation server or the advertising
asset server (not shown) and delivers them to users or clients
(114a-114n) or viewing websites. The ad server 111 is coupled to
the network 103 by signal line 118 for receiving ads from the ad
preparation server or the advertising asset server (not shown) and
for delivering the ads to third party servers, sites or domains
(not shown).
[0040] The content server 102 is a computer program running on a
hardware system for placing content on websites and/or placing the
data collection script 110 on web pages 109. For example, the
content server 102 may be a web server that provides the data
collection script 110 for publishers to place the data collection
script 110 on web browsers 108. The content server 102 is coupled
to the network 103 by signal line 124 for communication with the
other components of the system 100.
[0041] The data collection server 116 is a computer program running
on a hardware system for collecting data flow of samples that are
obtained by the data collection scripts 110. For example, the data
collection server 116 may be a web server that receives and gathers
sample data flow from various components in the distributed
environment. The data collection server 116 is coupled to the
network 103, by signal line 126, for communication with the other
components of the system 100.
[0042] FIG. 2A is a block diagram of example hardware components of
the statistical analytics server 104 (or a server dedicated to
determine or measure user-behavior on web-connected devices for
viewing advertisement online content). In this embodiment, the
statistical analytics server 104 comprises: a processor 202, memory
204 with a measurement engine 118, a network I/F module 208, an ad
and content database 117, and a bus 206. The processor 202
comprises an arithmetic logic unit, a microprocessor, a
general-purpose controller or some other processor array to perform
computations and provide electronic display signals to a display
device. The processor 202 is coupled to the bus 206 for
communication with the other components via a signal line. The
processor 202 processes data signals and may comprise various
computing architectures including a complex instruction set
computer (CISC) architecture, a reduced instruction set computer
(RISC) architecture, or an architecture implementing a combination
of instruction sets. Although only a single processor is shown in
FIG. 2, multiple processors may be included. Other processors,
operating systems, sensors, displays and physical configurations
are possible.
[0043] The memory 204 stores instructions and/or data that may be
executed by the processor 202. The memory 204 is coupled to the bus
206 via a signal line for communication with the other components
via a signal line. The instructions and/or data may comprise code
for performing any and/or all of the techniques described herein.
The memory 204 may be a dynamic random access memory (DRAM) device,
a static random access memory (SRAM) device, flash memory or some
other memory device.
[0044] The network I/F module 208, as illustrated, is coupled to
network 103, by a signal line, and is coupled to the bus 206. The
network The network I/F module 208 includes ports for wired
connectivity such as but not limited to USB, SD, or CAT-5, etc. The
network I/F module 208 links the processor 202 to the network 103
that may in turn be coupled to other processing systems. The
network I/F module 208 is configured to provide other connections
to the network 103 using standard network protocols such as TCP/IP,
HTTP, HTTPS and SMTP. In other embodiments, the network I/F module
208 includes a transceiver for sending and receiving signals using
Wi-Fi, Bluetooth.RTM. or cellular communications for wireless
communication. The network interface (I/F) module 208 provides a
communication path for the components of the client device 107a-n
to the network 103 and other systems.
[0045] The Ad and Content database 117 is data storage for storing
content and other data as illustrated in further detail with
reference to FIG. 7. The Ad and Content database 117 is coupled to
the bus 206. The Ad and Content storage 117 stores data,
information and instructions used by the processor 202. Such stored
information includes information about users, publishers, ads,
assets and other information. In one embodiment, the Ad and Content
storage 117 stores data received by the processor 202 as well as
data generated during intermediate processes. In one embodiment,
the Ad and Content database 117 is of conventional type. The Ad and
Content database 117 is a non-volatile memory or similar permanent
storage device and media such as a hard disk drive, a floppy disk
drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a
DVD-RW device, a flash memory device, or some other mass storage
device for storing information on a more permanent basis. The Ad
and Content database 117 is coupled by the bus 206 via a signal
line for communication with other components. The Ad and Content
database 117 will be described in further detail below with
reference to FIG. 7.
[0046] FIG. 2B is a block diagram illustrating example software
components of the measurement engine 118 of the statistical
analytics server 104, including a controller 220, an assessment
module 222, a dynamic sample rate management module 224, and a
statistical significance determination module 226.
[0047] The controller 220 is software, code or routines for
handling communications between the measurement engine 118 and
other components of the statistical analytics server 104. For
example, the controller 220 is coupled to receive sample data from
the data collection script 110 and deliver the sample data to one
or more other modules of the measurement engine 118, e.g., the
assessment module 222. In one embodiment, the controller 220
receives communication data from one or more other modules of the
measurement engine 118 and sends, via the network I/F module 208
(FIG. 2A), the communication data to the next entity. The
controller 220 is coupled to the bus 206 (FIG. 2A) for
communications with other components of the statistical analytics
server 104.
[0048] The assessment module 222 is software, code or routines for
assessing sample data that is received. The assessment module 222
is coupled by the network 103 to one or more client devices
107a-107n and thus one or more data collection scripts 110. Each of
the one or more data collection scripts 110 sends sample data to
the measurement engine 118 and the sample data is transmitted to
the assessment module 222 via the controller 220. In some
embodiments, the assessment module 222 processes the sample data.
The assessment module 222 of the measurement engine 118 makes
periodic assessments of its capacity versus demand and determines,
on its own, sample rates that are required, according to the same
algorithms used by the data collection script 110. It should be
recognized that the difference is only that the statistical
analytics server 104 is always operating and always aware of its
own capacity, and therefore, it can dynamically reduce the sample
rate according to its own assessed needs. The statistical analytics
server 104 is configured to attenuate its own input, by either
sampling at its own input, or instructing the distributed elements,
for example, the web browsers 108 to lower their sample rates. For
purpose of this specification, it should be recognized that
statistical significance is a function of the number of
observations/samples taken in a known universe representing a
population of objects to measure. The assessment module 222 of the
measurement engine 118 of the statistical analytics server 104 is
aware of the sample rates of data it receives, and can therefore,
it can assess whether to sample further, or not, depending on the
number of samples required for statistical validity.
[0049] The dynamic sample rate management module 224 is software,
code or routines for managing the sample rates as the need is
determined by the assessment module 222. The dynamic sample rate
management module 224 is coupled by the network 103 to one or more
client devices 107a-107n and thus one or more data collection
scripts 110. The dynamic sample rate management module 224 of the
measurement engine 118 of the statistical analytics server is
configured to weight samples to correct for bias in sampling that
might be created by downstream processes.
[0050] The statistical significance determination module 226 is
software, code or routines for determining the statistical
significance or characteristics of the sampling. It should be
recognized that statistical validity for the purposes of this
sampling is defined as an approximate match between an actual
distribution of samples taken, and an ideal distribution (e.g.,
represented by one or more thresholds that are determined or
established), per sampled element. The statistical significance (or
characteristic) determination module 226 of the measurement engine
118 of the statistical analytics server 104 periodically compares
the actual distribution to the ideal distribution. If the actual
distribution is either empirically or statistically different from
the ideal distribution at an input "confidence" level, the sample
rate is maintained or not lowered for the particular element for
which the sample is being obtained.
[0051] Referring now to FIG. 3A, example hardware and software
components of an example client device 107a-107n are illustrated.
An example client device 107a-107n may be of conventional type, a
computing device, for example, a personal computer, a hardware
server, a laptop computer, a tablet computer or smart phone. The
client devices 107a-107n, are coupled to the network 103 by signal
lines 116. The client devices 107a-107n include a processor 302,
memory 304, a network I/F module 308, a display device 310 on which
content is displayed or clients or users 114a-114n to view, and an
input device 312, via which data for the display device 310 is
received.
[0052] In one implementation, the client device 107 is coupled to
receive content with online advertisements from the ad server 111
and other content from publishing sites or third party servers (not
shown). The client device 107 includes the web browser 108 for
presenting web pages 109 including online content and
advertisements to the user or client 114a, 114b, through 114n. The
web browser 108 on each of the client or user device 107a-107n
presents advertisements and other content, and receives input from
the user or client 114a-114n. The web browser 108 and the data
collection script 110 on the web page 109 are operable on the
client devices 107a through 17n. In one embodiment, the data
collection script 110 may be embedded on the web browser 108 from
the script server 101. In another embodiment, the data collection
script 110 may be placed on the web browser 108 by the ad server
111. In yet another embodiment, the data collection script 110 may
be embedded on the web browser 108 by the content server 102.
[0053] The processor 302 comprises an arithmetic logic unit, a
microprocessor, a general-purpose controller or some other
processor array to perform computations and provide electronic
display signals to a display device. The processor 302 is coupled
to the bus 306 for communication with the other components via a
signal line. The processor 302 processes data signals and may
comprise various computing architectures including a complex
instruction set computer (CISC) architecture, a reduced instruction
set computer (RISC) architecture, or an architecture implementing a
combination of instruction sets. Although only a single processor
is shown in FIG. 3A, multiple processors may be included. Other
processors, operating systems, sensors, displays and physical
configurations are possible.
[0054] The memory 304 stores instructions and/or data that may be
executed by the processor 302. The memory 304 is coupled to the bus
306 via a signal line for communication with the other components
via a signal line. The instructions and/or data may comprise code
for performing any and/or all of the techniques described herein.
The memory 304 may be a dynamic random access memory (DRAM) device,
a static random access memory (SRAM) device, flash memory or some
other memory device.
[0055] The network I/F module 308 is coupled to network 103 by a
signal line 116 (a-n) and coupled to the bus 306. The network I/F
module 308 includes ports for wired connectivity such as but not
limited to USB, SD, or CAT-5, etc. The network I/F module 308 links
the processor 302 to the network 103 that may in turn be coupled to
other processing systems. The network I/F module 308 is configured
to provide other connections to the network 103 using standard
network protocols such as TCP/IP, HTTP, HTTPS and SMTP. In other
embodiments, the network I/F module 308 includes a transceiver for
sending and receiving signals using Wi-Fi, Bluetooth.RTM. or
cellular communications for wireless communication. The network
interface (I/F) module 308 provides a communication path for the
components of the client device 107a-n to the network 103 and other
systems.
[0056] Referring now to FIG. 3B, an example data collection script
110 is described in further detail. In the illustrated embodiment,
the data collection script 110 comprises a script loader, a content
determination module 322, a sampling module 324, and a data flow
management module 326. As noted above, the data collection script
110 is placed by a publisher or other entity on the web browser 108
of the client device 107 from one of the script server 101, the
content server 102 and the ad server 111. In some implementations,
the data collection script 110 is configured to be completely
autonomous in its operations. For example, the client device 107 is
used by a user 114 to run a web browser 108 for opening a web page
109. The data collection script 110 is placed on the web browser
108 to collect sample data.
[0057] The script loader 320 is software, code or routines for
loading the data collection script 110. For example, the script
loader 320 is coupled to receive a signal indicating that a web
page 109 is rendered from the web browser 108 and configured to
deliver the signal to one or more entities that may be configured
to load the data collection script 110. As indicated above, in
operation, the data collection script 110 configured to run on a
web page 109, renders in the browser 108 on a client device 107
a-n. It may be loaded by the script server 101, and begins to
execute.
[0058] The data collection script 110 is configured to determine if
sampling is required, by attempting to access a document provided
by the content server 102 connected to the network 103 and
containing instructions and recommended sample rates. In the event
no document is returned, the data collection script 110 uses a
"default" sample rate, which is embedded into the data collection
script programming itself. If the "default" sample rate is
determined to be 100%, then no sampling may be required for the
time being. In the event sampling is required, the data collection
script 110 is configured to randomly select objects to measure from
the objects of interest discovered at a rate corresponding to the
sample rate. For example, if the sample rate is determined to be
10%, then 1/10.sup.th of the available objects are sampled. The
objects to be sampled are selected by a random number.
[0059] The content determination module 322 is software, code, or
routines for determining content for which sampling measurements
are required, once the script is loaded. The content determination
module 322 is coupled to receive a signal indicating that a web
page 109 is rendered and coupled to determine content elements to
be measured on this web page 109. In some implementations, the
script loader 320 loads a data collection script from a script
server 101 and begins to execute. The content determination module
322 of the data collection script 110 first finds objects of
interest on the web page 109. These could be advertisements or
content elements. Derived characteristics of the element such as
"in-view time," "in-view rate," etc. are determined as the
numerical samples. Any one of several metrics might be determined
for sampling.
[0060] The sampling module 324 is software, code or routines for
sampling data as designated, either by the dictating entity (e.g.,
the content determination module 322) or as hard-wired within the
data collection script 110. The sampling module 324 then attempts
to determine if sampling is required by attempting to access a
document provided either by the content server 101 or otherwise
provided, containing instructions and recommended sample rates. If
no document is returned by the content server 101, the sampling
module 324 uses a default sample rate which in some implementations
may be embedded into the programming of the data collection script
110. If the default sample rate is determined to be 100% then no
sampling is required. The sampling module 324, if required to
sample, randomly selects objects to measure from the objects of
interest discovered at a rate corresponding to the sample rate. For
example, if the sample rate is 10% then 1/10.sup.th of the
available objects are sampled. The objects to be sampled are
selected by a random number.
[0061] The data flow management module 326 is software, code or
routines for managing the flow of data to write data to the
statistical analytic server 104 as indicated by the sampling module
324. In some instances, the statistical analytics server 104 aware
of its own capacity issues may select to further sample the
data.
[0062] Referring now to FIG. 4, one embodiment of a general method
400 in accordance with the present invention, for dynamically
controlling sample rates and data flow in a networked measurement
system by dynamic determination of statistical significance is
illustrated. The method 400 begins with one or more operations
designated by block 402, for determining objects of interest (e.g.,
advertisements, content elements) on a web page 109 that is
rendered. For example, once the web browser 108 renders a web page
109, the data collection script 110 begins to execute and receives
a signal indicating that a web page 109 is rendered. The method 400
continues by one or more operations for determining the sample
rate, as indicated by block 404. These operations are performed in
accordance with the programming and configurations for the web page
109. The method 400 proceeds and in accordance with one or more
operations designated by block 406 samples data at a rate that
corresponds to the sample rate that is determined. The method 400
proceeds to the next block 408 of one or more operations or
managing data flow by determining whether the sample data should be
provided to one or more components. The method 400 continues to
block 410 including one or more operations for performing
assessments to determine whether further sampling is required or
whether the sampling should be either reduced or increased. The
method proceeds to the next block 412 including one or more
operations for dynamically either reducing or increasing the sample
rates based on the assessments. The method proceeds to an
indication of "END," which is simply to illustrate an end to the
sequence of operations described above. It should be recognized
that the method 400 described is by way of example and it may
either include additional operations not described here or exclude
any of the operations that are described.
[0063] Referring now to FIGS. 5A and 5B, an example method 500
illustrating the sampling process by the data collection script 110
is illustrated and described. The method 500 begins by one or more
operations designated by block 502 or loading a script (e.g., data
collection script 110) from a script server (e.g., script server
101). The method 500 proceeds to the next block 504, including one
or more operations for determining objects of interest (e.g.,
advertisements, content elements) on a web page. The method 500
proceeds to the next block 506, including one or more operations
for deriving characteristics of objects of interest for numerical
samples. The method 500 proceeds to a decision block 508, including
one or more operations for determining if sampling is required. In
the event the answer is negative, the method 500 returns to block
504 and its operations to determine objects of interest. In the
event the answer at decision block 508 is affirmative, the method
500 proceeds to the next block 510 including one or more operations
for determining the sample rate. The method 500 proceeds to a next
block 512 including one or more operations for randomly selecting
objects to measure or sample from objects of interest at a rate
corresponding to a sample rate determined. From there, the method
500 proceeds to a next block 514, including one or more operations
for determining whether the sampled data should be provided to the
statistical analytics server. The method 500 proceeds to a
connector "A," and via the connector "A," to the next FIG. 5B,
where the method 500 continues.
[0064] Referring now to FIG. 5B, the method 500 proceeds to a
decision block 516, including one or more operations for
determining if there are instructions received from another client
device (e.g., any one of 107a-107n). In the event the answer is
affirmative, the method 500 proceeds to another decision block 520
including one or more operations for making a determination if data
should be provided to an analytics server (e.g., statistical
analytics server 104). In the event the answer is negative, the
method 500 proceeds to an "END," indicating an end to this sequence
of operations. In the event the answer in affirmative at the
decision block 520, the method 500 proceeds to the next block 526
including one or more operations for providing sampling data to the
analytics server (e.g., the statistical analytics server 104).
Returning to decision block 516, in the event the answer is
negative, the method 500 proceeds to the next block 518, including
one or more operations for determining if sampling data should be
provided to the analytics server (e.g., statistical analytical
server 104), based on interrogation (or querying) of the analytics
server (e.g., the statistical analytics server 104). In the event
the answer is affirmative, the method 500 proceeds to decision
block 520. Yet again, if the answer at the decision block 520 is
negative, the method 500 proceeds to an end. If the answer is
affirmative, the method 500 proceeds to the next block 526,
including one or more operations for providing sampling data to the
statistical analytics server (e.g., statistical analytics server
104). Returning to decision block 518, in the event the answer is
negative, the method 500 proceeds to another decision block 522,
including one or more operations for determining if the decision is
to be made by the data collection script 110. If the answer is
affirmative, the method 500 proceeds to the decision block 520
including one or more operations for determining if data should be
provided to the analytics server 520. In the event the answer is
negative, the method 500 proceeds to an end. In the event the
answer is affirmative, the method 500 proceeds to the next block
526 including one or more operations for providing the sampling
data to the analytics server. Returning to decision block 522, in
the event the answer at decision block 524 is negative, the method
proceeds to another decision block 524, including one or more
operations for determining if sampling should be performed based on
preset (i.e., hard-coded) instructions in the script or analytic
server (e.g., data collection script 110 or statistical analytics
server 104). In the event the answer is affirmative, again, the
method 500 proceeds to the decision block 520, including one or
more operations or determining whether sampling data should be
provided to the analytics server (e.g., statistical analytics
server 104). If the answer is negative, the method 500 proceeds to
an end. If the answer is affirmative, the method 500 proceeds to
the next block 526, including one or more operations for providing
sampling data to the analytics server. Returning to decision block
524, in the event the answer is negative, the method 500 proceeds
to an end. It should be recognized that the sequence of operations
illustrated in FIGS. 5A and 5B are only by way of example. The
sequence may be altered, or any of the operations either eliminated
or substituted by similar operations. The decision blocks in FIG.
5B reflect various possibilities, either or all of which may be
variously performed.
[0065] Referring now to FIG. 6, an example method 600 illustrating
a sampling process from the perspective of an analytics server
(e.g., the statistical analytics server 104) is described. The
method 600 begins and proceeds to a block 602 including one or more
operations for receiving sampling data. The method 600 proceeds to
the next block 604 for performing assessments to determine whether
further sampling (or reduced/increased sample rate) is required.
The method 600 proceeds to the next block 606 including one or more
operations for determining statistical validity or significance by
comparing actual distribution of sample that are taken and ideal
distribution per sampled element (e.g., by comparing with
established or predetermined thresholds). The method 600 proceeds
to the next block 608 including one or more operations for
determining if the actual distribution is empirically or
statistically different from ideal distribution at input confidence
level. If the answer is negative, the method 600 proceeds to an
End. If the answer at decision block 608 is affirmative, the method
600 returns to block 604 including one or more operations for
performing assessments to dynamically reduce/increase sample rates
and continues through blocks 606, and 608.
[0066] Referring now to FIG. 7, one embodiment of an Ad and Content
database 117 in accordance with the present invention is
illustrated and described. The Ad and Content database 117 is data
storage for storing data useful for the measurement engine 118 to
perform its functionality. In the illustrated embodiment, the Ad
and Content database 117 stores "Advertisements" 702, including,
for example, contextual advertisements that appear on search engine
results, page, banner advertisements, rich media advertisements,
social network advertising, online classified advertising etc. The
Ad and Content database 117 also includes "Content Elements" 704,
"Consumer Behavior (on Web-Connected Devices)" 706, "Media
Behavior" 708, "Sampled data" 710, and "Sample rates" 712 and other
types of data indicated by reference numeral 714. Examples of
content elements 704 are various text, images, sounds, videos,
animations, etc., which are present on a web page. Examples of
consumer behavior 708 include the way by which a user, client, or
consumer, interacts with content elements and/or advertisements
present on a web page 109. Examples of media behavior 708 include
the way by which content elements and/or advertisements are
displayed to a user/consumer. Examples of sampled data 710 include
consumer and media behaviors, advertisements, content elements,
etc., that are sampled as a means of gathering information. The
sample rates 712 include rates at which sampling should be
performed. Examples of other data include scripts, metrics such as
"in-view time," "in-view rate" etc., associated with sampled data
etc.
[0067] Systems and methods for measuring user behavior on
web-connected devices are described here. The systems and methods
determine advertising ("ad") and content visibility and other
indications of attention to or engagement with advertising or
content both within servers, and on network connections. In the
above description, for purposes of explanation, numerous specific
details were set forth. It will be apparent, however, that the
disclosed technologies can be practiced without these specific
details. In other instances, structures and devices are shown in
block diagram form. For example, the disclosed technologies are
described in one embodiment below with reference to user interfaces
and particular hardware. Moreover, the technologies are disclosed
above primarily in the context of the Internet and on-line
advertising; however, the disclosed technologies apply to other
types of advertising.
[0068] Reference in the specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment of the disclosed technologies.
The appearances of the phrase "in one embodiment" in various places
in the specification are not necessarily all referring to the same
embodiment.
[0069] Some portions of the detailed descriptions above were
presented in terms of processes and symbolic representations of
operations on data bits within a computer memory. A process can
generally be considered a self-consistent sequence of steps leading
to a result. The steps may involve physical manipulations of
physical quantities. These quantities take the form of electrical
or magnetic signals capable of being stored, transferred, combined,
compared, and otherwise manipulated. These signals may be referred
to as being in the form of bits, values, elements, symbols,
characters, terms, numbers or the like.
[0070] These and similar terms can be associated with the
appropriate physical quantities and can be considered labels
applied to these quantities. Unless specifically stated otherwise
as apparent from the prior discussion, it is appreciated that
throughout the description, discussions utilizing terms such as
"processing" or "computing" or "calculating" or "determining" or
"displaying" or the like, may refer to the action and processes of
a computer system, or similar electronic computing device, that
manipulates and transforms data represented as physical
(electronic) quantities within the computer system's registers and
memories into other data similarly represented as physical
quantities within the computer system memories or registers or
other such information storage, transmission or display
devices.
[0071] The disclosed technologies may also relate to an apparatus
for performing the operations herein. This apparatus may be
specially constructed for the required purposes, or it may comprise
a general-purpose computer selectively activated or reconfigured by
a computer program stored in the computer. Such a computer program
may be stored in a computer readable storage medium, such as, but
is not limited to, any type of disk including floppy disks, optical
disks, CD-ROMs, and magnetic disks, read-only memories (ROMs),
random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical
cards, flash memories including USB keys with non-volatile memory
or any type of media suitable for storing electronic instructions,
each coupled to a computer system bus.
[0072] The disclosed technologies can take the form of an entirely
hardware embodiment, an entirely software embodiment or an
embodiment containing both hardware and software elements. In one
embodiment, the technology is implemented in software, which
includes but is not limited to firmware, resident software,
microcode, etc.
[0073] Furthermore, the disclosed technologies can take the form of
a computer program product accessible from a computer-usable or
computer-readable medium providing program code for use by or in
connection with a computer or any instruction execution system. For
the purposes of this description, a computer-usable or
computer-readable medium can be any apparatus that can contain,
store, communicate, propagate, or transport the program for use by
or in connection with the instruction execution system, apparatus,
or device.
[0074] A data processing system suitable for storing and/or
executing program code will include at least one processor coupled
directly or indirectly to memory elements through a system bus. The
memory elements can include local memory employed during actual
execution of the program code, bulk storage, and cache memories
which provide temporary storage of at least some program code in
order to reduce the number of times code must be retrieved from
bulk storage during execution.
[0075] Input/output or I/O devices (including but not limited to
keyboards, displays, pointing devices, etc.) can be coupled to the
system either directly or through intervening I/O controllers.
[0076] Network adapters may also be coupled to the system to enable
the data processing system to become coupled to other data
processing systems or remote printers or storage devices through
intervening private or public networks. Modems, cable modems and
Ethernet cards are just a few of the currently available types of
network adapters.
[0077] Finally, the processes and displays presented herein may not
be inherently related to any particular computer or other
apparatus. Various general-purpose systems may be used with
programs in accordance with the teachings herein, or it may prove
convenient to construct more specialized apparatus to perform the
required method steps. The required structure for a variety of
these systems will appear from the description below. In addition,
the disclosed technologies were not described with reference to any
particular programming language. It will be appreciated that a
variety of programming languages may be used to implement the
teachings of the technologies as described herein.
[0078] The foregoing description of the embodiments of the present
techniques and technologies has been presented for the purposes of
illustration and description. It is not intended to be exhaustive
or to limit the present techniques and technologies to the precise
form disclosed. Many modifications and variations are possible in
light of the above teaching. It is intended that the scope of the
present techniques and technologies be limited not by this detailed
description. The present techniques and technologies may be
embodied in other specific forms without departing from the spirit
or essential characteristics thereof. Likewise, the particular
naming and division of the modules, routines, features, attributes,
methodologies and other aspects are not mandatory or significant,
and the mechanisms that implement the present techniques and
technologies or its features may have different names, divisions
and/or formats. Furthermore, the modules, routines, features,
attributes, methodologies and other aspects of the present
invention can be implemented as software, hardware, firmware or any
combination of the three. Also, wherever a component, an example of
which is a module, is implemented as software, the component can be
implemented as a standalone program, as part of a larger program,
as a plurality of separate programs, as a statically or dynamically
linked library, as a kernel loadable module, as a device driver,
and/or in every and any other way known now or in the future to
those of ordinary skill in the art of computer programming.
Additionally, the present techniques and technologies are in no way
limited to implementation in any specific programming language, or
for any specific operating system or environment. Accordingly, the
disclosure of the present techniques and technologies is intended
to be illustrative, but not limiting.
* * * * *